Week-7: Challenge
Chua Jieh Yih Audra
2023-10-02
A. About ggplot2
1. Load packages (Slide #6)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#install.packages("palmerpenguins")
library(palmerpenguins)
#look at the dataset
glimpse(penguins)## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…2. Palmer penguin plot recreation - data frame and axes with points (Slide #8-11)
ggplot(penguins) + aes(x = bill_depth_mm, y = bill_length_mm) + geom_point()## Warning: Removed 2 rows containing missing values (`geom_point()`).
3. Palmer penguin plot recreation - Map species to colour of point (Slide #12)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point()## Warning: Removed 2 rows containing missing values (`geom_point()`).
4. Palmer penguin plot recreation - labels (Slide #13-15)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)",
y = "Bill length (mm)")## Warning: Removed 2 rows containing missing values (`geom_point()`).
5. Palmer penguin plot recreation - Adding a legend (Slide #16)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)",
y = "Bill length (mm)",
colour = "Species")## Warning: Removed 2 rows containing missing values (`geom_point()`).
6. Palmer penguin plot recreation - Caption for data source (Slide #17)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)",
y = "Bill length (mm)",
colour = "Species",
caption = "Source: Palmer Station, Antarctica LTER")## Warning: Removed 2 rows containing missing values (`geom_point()`).
7. Palmer penguin plot recreation - Discrete colours for color blindness (Slide #18)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)",
y = "Bill length (mm)",
colour = "Species",
caption = "Source: Palmer Station, Antarctica LTER") +
scale_colour_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
8. Omitting first 2 arguments’ names when building plots (data, mapping) (Slide #20)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species) +
geom_point() +
labs(title = "Bill depth and length",
subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x = "Bill depth (mm)",
y = "Bill length (mm)",
colour = "Species",
caption = "Source: Palmer Station, Antarctica LTER") +
scale_colour_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
9. ggplot aesthetics options - Shape (Color alr shown above) (Slide #23)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species, shape = island) +
geom_point() +
scale_colour_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
10. aes options - Size & Shape (Slide #25)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species, shape = species, size = body_mass_g) +
geom_point() + scale_colour_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
11. aes options - Size, Shape, Alpha (Slide #26)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, colour = species, shape = species, size = body_mass_g, alpha = flipper_length_mm) +
geom_point() + scale_colour_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
12. Mapping VS Setting (Slide #28)
#Previous code was mapping:
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, size = body_mass_g, alpha = flipper_length_mm) +
geom_point()## Warning: Removed 2 rows containing missing values (`geom_point()`).
#These are settings:
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm) +
geom_point(size = 2, alpha = 0.5)## Warning: Removed 2 rows containing missing values (`geom_point()`).
13. Faceting (Slide #29)
# Facets are smaller plots that display subsets of the data; useful for exploring conditional relationships and large data
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm) +
geom_point() +
facet_grid(species ~ island)## Warning: Removed 2 rows containing missing values (`geom_point()`).
#rows ~ columns14. Faceting - Only one variable (Slide #32 and Slide #34)
#with facet_wrap
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm) +
geom_point() +
facet_wrap(~ species)## Warning: Removed 2 rows containing missing values (`geom_point()`).
#with facet_grid
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm) +
geom_point() +
facet_grid(. ~ species)## Warning: Removed 2 rows containing missing values (`geom_point()`).
15. Faceting - Wrapping w ncol (one variable) (Slide #33)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm) +
geom_point() +
facet_wrap(~ species, ncol = 2)## Warning: Removed 2 rows containing missing values (`geom_point()`).
16. Faceting - Facet and Color (Slide #35)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, color = species) +
geom_point() +
facet_grid(species ~ sex) +
#not necessary; js to change color scheme:
scale_color_viridis_d()## Warning: Removed 2 rows containing missing values (`geom_point()`).
17. Faceting - Remove the legend (Slide #37)
ggplot(penguins) +
aes(x = bill_depth_mm, y = bill_length_mm, color = species) +
geom_point() +
facet_grid(species ~ sex) +
scale_color_viridis_d() +
guides(color = "none")## Warning: Removed 2 rows containing missing values (`geom_point()`).
B. Visualizing numeric variables
18. Load and glimpse Lending Club data (Slide #39)
library(openintro)## Loading required package: airports## Loading required package: cherryblossom## Loading required package: usdataglimpse(loans_full_schema)## Rows: 10,000
## Columns: 55
## $ emp_title <chr> "global config engineer ", "warehouse…
## $ emp_length <dbl> 3, 10, 3, 1, 10, NA, 10, 10, 10, 3, 1…
## $ state <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, I…
## $ homeownership <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN…
## $ annual_income <dbl> 90000, 40000, 40000, 30000, 35000, 34…
## $ verified_income <fct> Verified, Not Verified, Source Verifi…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.4…
## $ annual_income_joint <dbl> NA, NA, NA, NA, 57000, NA, 155000, NA…
## $ verification_income_joint <fct> , , , , Verified, , Not Verified, , ,…
## $ debt_to_income_joint <dbl> NA, NA, NA, NA, 37.66, NA, 13.12, NA,…
## $ delinq_2y <int> 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0…
## $ months_since_last_delinq <int> 38, NA, 28, NA, NA, 3, NA, 19, 18, NA…
## $ earliest_credit_line <dbl> 2001, 1996, 2006, 2007, 2008, 1990, 2…
## $ inquiries_last_12m <int> 6, 1, 4, 0, 7, 6, 1, 1, 3, 0, 4, 4, 8…
## $ total_credit_lines <int> 28, 30, 31, 4, 22, 32, 12, 30, 35, 9,…
## $ open_credit_lines <int> 10, 14, 10, 4, 16, 12, 10, 15, 21, 6,…
## $ total_credit_limit <int> 70795, 28800, 24193, 25400, 69839, 42…
## $ total_credit_utilized <int> 38767, 4321, 16000, 4997, 52722, 3898…
## $ num_collections_last_12m <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ num_historical_failed_to_pay <int> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ months_since_90d_late <int> 38, NA, 28, NA, NA, 60, NA, 71, 18, N…
## $ current_accounts_delinq <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ total_collection_amount_ever <int> 1250, 0, 432, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ current_installment_accounts <int> 2, 0, 1, 1, 1, 0, 2, 2, 6, 1, 2, 1, 2…
## $ accounts_opened_24m <int> 5, 11, 13, 1, 6, 2, 1, 4, 10, 5, 6, 7…
## $ months_since_last_credit_inquiry <int> 5, 8, 7, 15, 4, 5, 9, 7, 4, 17, 3, 4,…
## $ num_satisfactory_accounts <int> 10, 14, 10, 4, 16, 12, 10, 15, 21, 6,…
## $ num_accounts_120d_past_due <int> 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, …
## $ num_accounts_30d_past_due <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ num_active_debit_accounts <int> 2, 3, 3, 2, 10, 1, 3, 5, 11, 3, 2, 2,…
## $ total_debit_limit <int> 11100, 16500, 4300, 19400, 32700, 272…
## $ num_total_cc_accounts <int> 14, 24, 14, 3, 20, 27, 8, 16, 19, 7, …
## $ num_open_cc_accounts <int> 8, 14, 8, 3, 15, 12, 7, 12, 14, 5, 8,…
## $ num_cc_carrying_balance <int> 6, 4, 6, 2, 13, 5, 6, 10, 14, 3, 5, 3…
## $ num_mort_accounts <int> 1, 0, 0, 0, 0, 3, 2, 7, 2, 0, 2, 3, 3…
## $ account_never_delinq_percent <dbl> 92.9, 100.0, 93.5, 100.0, 100.0, 78.1…
## $ tax_liens <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ public_record_bankrupt <int> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ loan_purpose <fct> moving, debt_consolidation, other, de…
## $ application_type <fct> individual, individual, individual, i…
## $ loan_amount <int> 28000, 5000, 2000, 21600, 23000, 5000…
## $ term <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 3…
## $ interest_rate <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.7…
## $ installment <dbl> 652.53, 167.54, 71.40, 664.19, 786.87…
## $ grade <fct> C, C, D, A, C, A, C, B, C, A, C, B, C…
## $ sub_grade <fct> C3, C1, D1, A3, C3, A3, C2, B5, C2, A…
## $ issue_month <fct> Mar-2018, Feb-2018, Feb-2018, Jan-201…
## $ loan_status <fct> Current, Current, Current, Current, C…
## $ initial_listing_status <fct> whole, whole, fractional, whole, whol…
## $ disbursement_method <fct> Cash, Cash, Cash, Cash, Cash, Cash, C…
## $ balance <dbl> 27015.86, 4651.37, 1824.63, 18853.26,…
## $ paid_total <dbl> 1999.330, 499.120, 281.800, 3312.890,…
## $ paid_principal <dbl> 984.14, 348.63, 175.37, 2746.74, 1569…
## $ paid_interest <dbl> 1015.19, 150.49, 106.43, 566.15, 754.…
## $ paid_late_fees <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…19. Select some variables (Slide #40)
loans <- loans_full_schema %>%
select(loan_amount, interest_rate, term, grade, state, annual_income, homeownership, debt_to_income)20. Histogram (Slide #46-47)
ggplot(loans) +
aes(x = loan_amount) +
geom_histogram(binwidth = 1000)
#binwidth to change... bin width.21. Filling a histogram with a categorical variable (Slide #51)
ggplot(loans) +
aes(x = loan_amount, fill = homeownership) +
geom_histogram(binwidth = 5000, alpha = 0.5) +
labs(x = "Loan amount ($)",
y = "Frequency",
title = "Amounts of Lending Club loans")
22. Histogram: Facet with a categorical variable (Slide #52)
ggplot(loans) +
aes(x = loan_amount, fill = homeownership) +
geom_histogram(binwidth = 1000, alpha = 0.5) +
labs(x = "Loan amount ($)",
y = "Frequency",
title = "Amounts of Lending Club loans") +
facet_wrap(~ homeownership, nrow = 3)
23. Density plot (Slide #53)
ggplot(loans) +
aes(x = loan_amount) +
geom_density()
23. Density plot - Adjust bandwidth (Slide #54)
ggplot(loans) +
aes(x = loan_amount) +
geom_density(adjust = 0.5)
#1 is default bandwidth23. Density plot - Customising (labs) (Slide #57)
ggplot(loans) +
aes(x = loan_amount) +
geom_density(adjust = 1.5) +
labs( x = "Loan amount ($)",
y = "Density",
title = "Amounts of Lending Club loans" )
23. Density plot - Adding a categorical variable (Slide #58)
ggplot(loans) +
aes(x = loan_amount, fill = homeownership) +
geom_density(adjust = 1.5, alpha = 0.4) +
labs( x = "Loan amount ($)",
y = "Density",
title = "Amounts of Lending Club loans" )
23. Box plot (Slide #59)
ggplot(loans) +
aes(x = interest_rate) +
geom_boxplot()
23. Box plot - Customizing (labs, theme) (Slide #61)
ggplot(loans) +
aes(x = interest_rate) +
geom_boxplot() +
labs(x = "Interest rate (%)",
y = NULL,
title = "Interest rates of Lending Club loans") +
theme(axis.ticks.y = element_blank(), axis.text.y = element_blank() )
23. Box plot - Adding a categoric variable (Slide #62)
ggplot(loans) +
aes(x = interest_rate, y = grade) +
geom_boxplot() +
labs(x = "Interest rate (%)",
y = "Grade",
title = "Interest rates of Lending Club loans",
subtitle = "by grade of loan")
23. Scatterplot (Slide #63)
ggplot(loans) +
aes(x = debt_to_income, y = interest_rate) +
geom_point()## Warning: Removed 24 rows containing missing values (`geom_point()`).
23. Hex plot (Slide #64-65)
#with a filter
ggplot(loans %>% filter(debt_to_income < 100)) +
aes(x = debt_to_income, y = interest_rate) +
geom_hex()
C. Visualizing categoric variables
23. Bar plot (Slide #67)
ggplot(loans) +
aes(x = homeownership) +
geom_bar()
23. Segmented bar plot (Slide #68)
ggplot(loans) +
aes(x = homeownership, fill = grade) +
geom_bar()
23. Segmented bar plot - comparison optimising (Slide #69)
ggplot(loans) +
aes(x = homeownership, fill = grade) +
geom_bar(position = "fill")
23. Customizing bar plots (Slide #70)
ggplot(loans) +
aes(y = homeownership, fill = grade) +
geom_bar(position = "fill") +
labs(x = "Proportion",
y = "Homeownership",
fill = "Grade",
title = "Grades of Lending Club loans",
subtitle = "and homeownership of lendee")
D. Visualizing variables of varied types
23. Violin plot (Slide #73)
ggplot(loans) +
aes(x = homeownership, y = loan_amount) +
geom_violin()
23. Ridge plot (Slide #74)
library(ggridges)
ggplot(loans) +
aes(x = loan_amount, y = grade, fill = grade, color = grade) +
geom_density_ridges(alpha = 0.5)## Picking joint bandwidth of 2360